RAW OVER COMPOSITE: A DAILY LLM BENCHMARK REPO YOU CAN ACTUALLY TRUST
Iternal launched a daily-updated LLM Benchmark Repository that shows raw scores with source links instead of composite "intelligence" indexes. The new [LLM Ben...
Iternal launched a daily-updated LLM Benchmark Repository that shows raw scores with source links instead of composite "intelligence" indexes.
The new LLM Benchmark Repository aggregates MMLU‑Pro, GPQA, SWE‑bench, Chatbot Arena ELO, and more, with full attribution and CSV export—no opaque rollups. It argues composites hide sparsity and mix incomparable scales.
If you still watch coding leaderboards like Kilo’s picks and usage stats, this is a better cross‑check before you buy into “top model” claims. Even recent hype videos on “new benchmarks breaking frontier models” example show how noisy the space is. Start from raw scores tied to the tests that match your work.
Composite leaderboards can hide gaps; raw scores with sources make model selection less guessy.
You can align model picks to the specific benchmarks that map to your workloads.
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terminal
Pull the CSV and compare your shortlist on SWE-bench Verified, GPQA, and MMLU-Pro, weighting by your use case.
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terminal
Correlate repository scores with a small internal eval set to see which benchmarks actually predict your success.
Legacy codebase integration strategies...
- 01.
Re-audit current model choices against raw benchmark coverage; watch for sparsity behind headline numbers.
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Set alerts to re-check when providers update models and scores shift.
Fresh architecture paradigms...
- 01.
Use raw benchmark filters to narrow candidates, then run a task-specific bakeoff before integrating.
- 02.
Design your eval harness to mirror one or two public benchmarks you trust for ongoing comparability.
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